Another question we had was to see whether splitting into smaller, more extreme groups would show morer extreme differences across them.

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(reshape2)
library(psych)
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
library(patchwork)
library(rockchalk)
## 
## Attaching package: 'rockchalk'
## The following object is masked from 'package:dplyr':
## 
##     summarize
load('data/load_effects_DFR.RData')
load('data/behav.RData')
load('data/structural_measures.RData')
load('data/connectivity_data.RData')

source("split_into_groups.R")
source("prep_split_for_bar_plots.R")
source("plot_bars.R")
constructs_fMRI <- construct_vars_omnibus[construct_vars_omnibus$PTID %in% p200_indiv_ROI_DFR_delay$PTID,]

data_for_plot <- merge(p200_indiv_ROI_DFR_delay,constructs_fMRI)
data_for_plot <- merge(data_for_plot,things_to_hist[,c(1,8)],by="PTID",all=TRUE)

data_for_plot$level <- factor(data_for_plot$level, levels = c("high","med","low"))

p1 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_ROIs))+
  geom_point(aes(color=level))+
  stat_smooth(method="loess")

p2 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_dlPFC))+
  geom_point(aes(color=level))+
  stat_smooth(method="loess")

p3 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_IPS))+
  geom_point(aes(color=level))+
  stat_smooth(method="loess")


p4 <- ggplot(data_for_plot, aes(x=omnibus_span_no_DFR_MRI, y = DFR_L_preSMA))+
  geom_point(aes(color=level))+
  stat_smooth(method="loess")

(p1+p2)/(p3+p4)

split_constructs <- split_into_groups(constructs_fMRI[1:7],extreme_groups)
split_clinical <- split_into_groups(p200_clinical_zscores, extreme_groups)
split_DFR_delay <- split_into_groups(p200_indiv_ROI_DFR_delay, extreme_groups)
split_DFR_cue <- split_into_groups(p200_indiv_ROI_DFR_cue, extreme_groups)
split_DFR_probe <- split_into_groups(p200_indiv_ROI_DFR_probe, extreme_groups)
split_DFR_FFA <- split_into_groups(p200_FFA,extreme_groups)
split_DFR_HPC_Ant <- split_into_groups(p200_HPC_Ant, extreme_groups)
split_DFR_HPC_Med <- split_into_groups(p200_HPC_Med, extreme_groups)
split_DFR_HPC_Post <- split_into_groups(p200_HPC_Post, extreme_groups)
split_fullMask_delay <- split_into_groups(p200_DFR_full_mask, extreme_groups)
split_cue_ROIs <- split_into_groups(p200_indiv_ROI_delayDFR_cuePeriod, extreme_groups)
split_demographics <- split_into_groups(p200_demographics,extreme_groups)
split_cortical_thickness_DFR <- split_into_groups(p200_DFR_fullMask_cortical_thickness,extreme_groups)
split_RS <- split_into_groups(p200_all_RS,extreme_groups)
split_beta_conn_cue <- split_into_groups(p200_beta_conn_cue,extreme_groups)
split_beta_conn_delay <- split_into_groups(p200_beta_conn_delay,extreme_groups)
split_BCT <- split_into_groups(p200_BCT_forCorr,extreme_groups)
split_indiv_partic_coeff <- split_into_groups(p200_indiv_network_ParticCoeff,extreme_groups)
save(list=c("split_constructs","split_clinical","split_DFR_delay", "split_DFR_cue", "split_DFR_probe", "split_DFR_FFA", "split_DFR_HPC_Ant", "split_DFR_HPC_Med", "split_DFR_HPC_Post", "split_fullMask_delay", "split_cue_ROIs", "split_demographics","split_cortical_thickness_DFR","split_RS","split_beta_conn_cue","split_beta_conn_delay","split_BCT", "split_indiv_partic_coeff"), file="data/split_extreme_groups_fMRI.RData")
split_means_demo <- data.frame(matrix(nrow=length(split_demographics)-1,ncol=8))
colnames(split_means_demo) <- c("Trio","Prisma","CS","NCS","female","male","age","age_se")
rownames(split_means_demo) <- names(split_demographics)[1:length(names(split_demographics))-1]

for (level in seq.int(1,length(split_demographics)-1)){
  split_means_demo$Trio[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==1])
  split_means_demo$Prisma[level] <- length(split_demographics[[level]]$SCANNER[split_demographics[[level]]$SCANNER==2])
  split_means_demo$CS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==1])
  split_means_demo$NCS[level] <- length(split_demographics[[level]]$GROUP[split_demographics[[level]]$GROUP==2])
  split_means_demo$female[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==2])
  split_means_demo$male[level] <- length(split_demographics[[level]]$GENDER[split_demographics[[level]]$GENDER==1])
  split_means_demo$age[level] <- mean(split_demographics[[level]]$AGE,na.rm=TRUE)
  split_means_demo$age_se[level] <- sd(split_demographics[[level]]$AGE,na.rm=TRUE)/sqrt(length(split_demographics[[level]]$AGE[!is.na(split_demographics[[level]]$AGE)]))
  
}

split_means_demo$level <- as.factor(c("high", "med","low"))
means_melt_demo <- melt(split_means_demo,id.vars="level")

Demographics

age_plot <- ggplot(data=split_means_demo,aes(x=level,y=age))+
  geom_bar(stat="identity",width = .5, color = "#667Ea4", fill = "#667Ea4")+
  geom_errorbar(aes(ymin=age-age_se,ymax=age+age_se),width=.2)+
  ggtitle("Age") +
  ylab("Mean +/- SE") +
  scale_x_discrete(limits = c("low","med","high")) +
  theme(aspect.ratio = 1)

scanner_data <- demo_plot_data.m[demo_plot_data.m$variable=="scanner_count",c(1,2,5,6)]
scanner_data$value <- scanner_data$value/56*100
scanner_plot <- ggplot(scanner_data,aes(x=level,y=value,fill=scanner))+
  geom_bar(stat="identity") +
  ylab("Percent (%)") +
  theme(aspect.ratio=1) +
  scale_x_discrete(limits = c("low","med","high")) +
  ggtitle("Scanner")

gender_data <- demo_plot_data.m[demo_plot_data.m$variable=="gender_count",c(1,3,5,6)]
gender_data$value <- gender_data$value/56*100
gender_plot <- ggplot(gender_data,aes(x=level,y=value, fill=gender))+
  geom_bar(stat="identity") +
  ylab("Percent (%)") +
  theme(aspect.ratio=1) +
  scale_x_discrete(limits = c("low","med","high")) +
  ggtitle("Gender")


care_data <- demo_plot_data.m[demo_plot_data.m$variable=="care_count",c(1,4:6)]
care_data$value <- care_data$value/56*100
care_plot <- ggplot(care_data,aes(x=level,y=value, fill=care))+
  geom_bar(stat="identity") +
  ylab("Percent (%)") +
  theme(aspect.ratio=1) +
  scale_x_discrete(limits = c("low","med","high")) +
  ggtitle("CS vs NCS")

(age_plot + gender_plot)/(care_plot + scanner_plot)+
  plot_annotation(title="Demographics split by DFR performance")

melt_constructs <- prep_split_for_bar_plots(extreme_groups)
melt_clinical <- prep_split_for_bar_plots(split_clinical)
melt_DFR_delay <- prep_split_for_bar_plots(split_DFR_delay)
melt_DFR_cue <- prep_split_for_bar_plots(split_DFR_cue)
melt_DFR_probe <- prep_split_for_bar_plots(split_DFR_probe)
melt_DFR_FFA <- prep_split_for_bar_plots(split_DFR_FFA)
melt_DFR_HPC_Ant <- prep_split_for_bar_plots(split_DFR_HPC_Ant)
melt_DFR_HPC_Med <- prep_split_for_bar_plots(split_DFR_HPC_Med)
melt_DFR_HPC_Post <- prep_split_for_bar_plots(split_DFR_HPC_Post)
melt_fullMask_delay <- prep_split_for_bar_plots(split_fullMask_delay)
melt_cue_ROIs <- prep_split_for_bar_plots(split_cue_ROIs)
melt_cortical_thickness_DFR <- prep_split_for_bar_plots(split_cortical_thickness_DFR)
melt_RS <- prep_split_for_bar_plots(split_RS)
melt_beta_conn_cue <- prep_split_for_bar_plots(split_beta_conn_cue)
melt_beta_conn_delay <- prep_split_for_bar_plots(split_beta_conn_delay)
melt_BCT <- prep_split_for_bar_plots(split_BCT)
melt_indiv_partic_coeff <- prep_split_for_bar_plots(split_indiv_partic_coeff)
constructs_plots <- plot_bars(melt_constructs)
clinical_plots <- plot_bars(melt_clinical)
DFR_delay_plots <- plot_bars(melt_DFR_delay)
DFR_cue_plots <- plot_bars(melt_DFR_cue)
DFR_probe_plots <- plot_bars(melt_DFR_probe)
DFR_FFA_plots <- plot_bars(melt_DFR_FFA)
DFR_HPC_Ant_plots <- plot_bars(melt_DFR_HPC_Ant)
DFR_HPC_Med_plots <- plot_bars(melt_DFR_HPC_Med)
DFR_HPC_Post_plots <- plot_bars(melt_DFR_HPC_Post)
fullMask_delay_plots <- plot_bars(melt_fullMask_delay)
cue_ROIs_plots <- plot_bars(melt_cue_ROIs)
cortical_thickness_plots <- plot_bars(melt_cortical_thickness_DFR)
RS_plots <- plot_bars(melt_RS)
beta_conn_cue_plots <- plot_bars(melt_beta_conn_cue)
beta_conn_delay_plots <- plot_bars(melt_beta_conn_delay)
BCT_plots <- plot_bars(melt_BCT)
indiv_partic_coeff_plots <- plot_bars(melt_indiv_partic_coeff)

Constructs

A nice sanity check here as well - if a subject has higher capacity and higher intelligence, they tend to have higher performance. The main statistically significant differences here are in omnibus span, where high > low.

constructs_plots[["omnibus_span_no_DFR_MRI"]]$labels$title = "Omnibus Span"

(constructs_plots[["omnibus_span_no_DFR_MRI"]]+constructs_plots[["intelligence"]]+constructs_plots[["LTM"]]) +
  plot_annotation(title="Constructs split on DFR performance")

print("Omnibus Span")
## [1] "Omnibus Span"
span.aov <- aov(omnibus_span_no_DFR_MRI ~ level, data=split_constructs[["all"]])
summary(span.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)    
## level         2  40.24  20.121   413.8 <2e-16 ***
## Residuals   117   5.69   0.049                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(span.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = omnibus_span_no_DFR_MRI ~ level, data = split_constructs[["all"]])
## 
## $level
##                diff        lwr        upr p adj
## med-high -0.6960934 -0.8131510 -0.5790359     0
## low-high -1.4184201 -1.5354776 -1.3013625     0
## low-med  -0.7223266 -0.8393842 -0.6052691     0
print("LTM")
## [1] "LTM"
LTM.aov <- aov(LTM ~ level, data=split_constructs[["all"]])
summary(LTM.aov)
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## level         2   7.55   3.776   7.377 0.000971 ***
## Residuals   114  58.34   0.512                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
TukeyHSD(LTM.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = LTM ~ level, data = split_constructs[["all"]])
## 
## $level
##                diff        lwr         upr     p adj
## med-high -0.4460251 -0.8283300 -0.06372028 0.0178226
## low-high -0.5959517 -0.9807951 -0.21110828 0.0010451
## low-med  -0.1499266 -0.5371662  0.23731309 0.6291854
print("Intelligence")
## [1] "Intelligence"
intelligence.aov <- aov(intelligence ~ level, data=split_constructs[["all"]])
summary(intelligence.aov)
##              Df Sum Sq Mean Sq F value   Pr(>F)    
## level         2  23.01  11.506   30.08 2.88e-11 ***
## Residuals   117  44.75   0.382                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(intelligence.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = intelligence ~ level, data = split_constructs[["all"]])
## 
## $level
##                diff        lwr         upr     p adj
## med-high -0.3647352 -0.6930224 -0.03644802 0.0255139
## low-high -1.0559533 -1.3842405 -0.72766613 0.0000000
## low-med  -0.6912181 -1.0195053 -0.36293091 0.0000061

Clinical

clinical_plots[["WHO_ST_S32"]]$labels$title <- "WHODAS"
clinical_plots[["BPRS"]]$labels$title <- "BPRS"

(clinical_plots[["WHO_ST_S32"]] + clinical_plots[["BPRS_TOT"]])+
  plot_annotation(title="Clinical measures split by DFR performance")

print("WHODAS")
## [1] "WHODAS"
WHODAS.aov <- aov(WHO_ST_S32 ~ level, data=split_clinical[["all"]])
summary(WHODAS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   3.76   1.880   1.873  0.158
## Residuals   117 117.46   1.004
print("BPRS")
## [1] "BPRS"
BPRS.aov <- aov(BPRS_TOT ~ level, data=split_clinical[["all"]])
summary(BPRS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   8.75   4.373   4.261 0.0164 *
## Residuals   117 120.08   1.026                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Cue Period

Full mask

There is a linear relationship between performance and load effect in the cue mask during the delay period.

fullMask_delay_plots[["cue_low"]]+fullMask_delay_plots[["cue_high"]]+fullMask_delay_plots[["cue_loadEffect"]]+
  plot_annotation(title="BOLD signal from full delay period mask during cue period")

print("Load Effect")
## [1] "Load Effect"
cue_LE.aov <- aov(cue_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.409  0.2044   1.429  0.244
## Residuals   117 16.737  0.1431
TukeyHSD(cue_LE.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = cue_loadEffect ~ level, data = split_fullMask_delay[["all"]])
## 
## $level
##                 diff        lwr       upr     p adj
## med-high  0.05027093 -0.1504947 0.2510366 0.8233839
## low-high -0.09078882 -0.2915545 0.1099768 0.5323653
## low-med  -0.14105976 -0.3418254 0.0597059 0.2218776

Individual ROIs

DFR_cue_plots[["L_FEF_low"]] +  DFR_cue_plots[["L_FEF_high"]] + DFR_cue_plots[["L_FEF_loadEffect"]]

DFR_cue_plots[["L_insula_low"]] +  DFR_cue_plots[["L_insula_high"]] + DFR_cue_plots[["L_insula_loadEffect"]]

DFR_cue_plots[["L_IPS_low"]] +  DFR_cue_plots[["L_IPS_high"]] + DFR_cue_plots[["L_IPS_loadEffect"]]

DFR_cue_plots[["L_occipital_low"]] +  DFR_cue_plots[["L_occipital_high"]] + DFR_cue_plots[["L_occipital_loadEffect"]]

DFR_cue_plots[["R_FEF_low"]] +  DFR_cue_plots[["R_FEF_high"]] + DFR_cue_plots[["R_FEF_loadEffect"]]

DFR_cue_plots[["R_insula_low"]] +  DFR_cue_plots[["R_insula_high"]] + DFR_cue_plots[["R_insula_loadEffect"]]

DFR_cue_plots[["R_IPS_low"]] +  DFR_cue_plots[["R_IPS_high"]] + DFR_cue_plots[["R_IPS_loadEffect"]]

DFR_cue_plots[["R_MFG_low"]] +  DFR_cue_plots[["R_MFG_high"]] + DFR_cue_plots[["R_MFG_loadEffect"]]

DFR_cue_plots[["R_preSMA_low"]] +  DFR_cue_plots[["R_preSMA_high"]] + DFR_cue_plots[["R_preSMA_loadEffect"]]

DFR_cue_plots[["R_occipital_low"]] +  DFR_cue_plots[["R_occipital_high"]] + DFR_cue_plots[["R_occipital_loadEffect"]]

print("L FEF")
## [1] "L FEF"
cue_L_FEF.aov <- aov(L_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_FEF.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.067 0.03369   0.156  0.856
## Residuals   117 25.300 0.21624
print("L insula")
## [1] "L insula"
cue_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_insula.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.12 0.06129   0.224    0.8
## Residuals   117  32.06 0.27403
print("L IPS")
## [1] "L IPS"
cue_L_IPS.aov <- aov(L_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_IPS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2    0.1  0.0506   0.114  0.892
## Residuals   117   51.9  0.4436
print("L occipital")
## [1] "L occipital"
cue_L_occipital.aov <- aov(L_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_L_occipital.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.34  0.6688   1.389  0.253
## Residuals   117  56.33  0.4815
print("R FEF")
## [1] "R FEF"
cue_R_FEF.aov <- aov(R_FEF_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_FEF.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.32  0.1621   0.264  0.769
## Residuals   117  71.97  0.6151
print("R insula")
## [1] "R insula"
cue_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_insula.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.031 0.01563   0.066  0.936
## Residuals   117 27.518 0.23520
print("R IPS")
## [1] "R IPS"
cue_R_IPS.aov <- aov(R_IPS_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_IPS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.11  0.0540   0.122  0.885
## Residuals   117  51.75  0.4423
print("R MFG")
## [1] "R MFG"
cue_R_MFG.aov <- aov(R_MFG_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_MFG.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.278  0.1389    0.72  0.489
## Residuals   117 22.578  0.1930
print("R occipital")
## [1] "R occipital"
cue_R_occipital.aov <- aov(R_occipital_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_occipital.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.74  0.3676   0.798  0.453
## Residuals   117  53.92  0.4608
print("R preSMA")
## [1] "R preSMA"
cue_R_preSMA.aov <- aov(R_preSMA_loadEffect ~ level, data=split_DFR_cue[["all"]])
summary(cue_R_preSMA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.21  0.1053   0.215  0.807
## Residuals   117  57.31  0.4898

Delay Period

Full Mask

There are significant differnces in the high load and load effect - the high load trials have differences: high > low and high > medium, while the load effect only has high > low.

fullMask_delay_plots[["delay_low"]]+fullMask_delay_plots[["delay_high"]]+fullMask_delay_plots[["delay_loadEffect"]]+
  plot_annotation(title="BOLD signal from full delay period mask during delay period")

print("Low Load")
## [1] "Low Load"
delay_L1.aov <- aov(delay_low ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L1.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0525 0.02627   1.041  0.356
## Residuals   117 2.9537 0.02525
print("High Load")
## [1] "High Load"
delay_L3.aov <- aov(delay_high ~ level, data=split_fullMask_delay[["all"]])
summary(delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.127 0.06364   1.594  0.208
## Residuals   117  4.672 0.03993
print("Load Effect")
## [1] "Load Effect"
delay_LE.aov <- aov(delay_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.260  0.1301    2.53  0.084 .
## Residuals   117  6.014  0.0514                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Individual ROIs

No L dMFG, all show high > low, except for . L aMFG, L dlPFC, R dlPFC also showed high > med, and R medial parietal only showed high > med.

(DFR_delay_plots[["DFR_L_aMFG"]] + DFR_delay_plots[["DFR_L_dlPFC"]] + DFR_delay_plots[["DFR_L_dMFG"]]) + plot_annotation(title="individual DFR delay period ROIs")

(DFR_delay_plots[["DFR_L_IPS"]] + DFR_delay_plots[["DFR_L_preSMA"]] + DFR_delay_plots[["DFR_R_dlPFC"]])  

(DFR_delay_plots[["DFR_R_dMFG"]] + DFR_delay_plots[["DFR_R_IPS"]] + DFR_delay_plots[["DFR_R_medParietal"]]) 

print("L aMFG")
## [1] "L aMFG"
L_aMFG.aov <- aov(DFR_L_aMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_aMFG.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.704  0.3518   4.144 0.0182 *
## Residuals   117  9.932  0.0849                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_aMFG.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_L_aMFG ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff         lwr         upr     p adj
## med-high  0.05621849 -0.09844019  0.21087717 0.6646739
## low-high -0.12685026 -0.28150895  0.02780842 0.1302187
## low-med  -0.18306876 -0.33772744 -0.02841007 0.0159052
print("L dlPFC")
## [1] "L dlPFC"
L_dlPFC.aov <- aov(DFR_L_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(L_dlPFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.278 0.13909   1.927   0.15
## Residuals   117  8.445 0.07218
print("L dMFG")
## [1] "L dMFG"
L_dMFG.aov <- aov(DFR_L_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(L_dMFG.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.308 0.15397   2.839 0.0625 .
## Residuals   117  6.346 0.05424                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("L IPS")
## [1] "L IPS"
L_IPS.aov <- aov(DFR_L_IPS ~ level, data=split_DFR_delay[["all"]])
summary(L_IPS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.363 0.18152   3.362  0.038 *
## Residuals   117  6.317 0.05399                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_IPS.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_L_IPS ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff         lwr          upr     p adj
## med-high  0.09314226 -0.03019883  0.216483355 0.1764976
## low-high -0.03773438 -0.16107547  0.085606715 0.7484446
## low-med  -0.13087664 -0.25421773 -0.007535549 0.0347881
print("L preSMA")
## [1] "L preSMA"
L_preSMA.aov <- aov(DFR_L_preSMA ~ level, data=split_DFR_delay[["all"]])
summary(L_preSMA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.393 0.19670   3.298 0.0404 *
## Residuals   117  6.978 0.05964                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_preSMA.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = DFR_L_preSMA ~ level, data = split_DFR_delay[["all"]])
## 
## $level
##                 diff         lwr          upr     p adj
## med-high  0.03055281 -0.09907881  0.160184442 0.8417916
## low-high -0.10326650 -0.23289813  0.026365129 0.1457828
## low-med  -0.13381931 -0.26345094 -0.004187685 0.0413687
print("R dlPFC")
## [1] "R dlPFC"
R_dlPFC.aov <- aov(DFR_R_dlPFC ~ level, data=split_DFR_delay[["all"]])
summary(R_dlPFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.339 0.16943   1.993  0.141
## Residuals   117  9.944 0.08499
print("R dMFG")
## [1] "R dMFG"
R_dMFG.aov <- aov(DFR_R_dMFG ~ level, data=split_DFR_delay[["all"]])
summary(R_dMFG.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.201 0.10060   1.359  0.261
## Residuals   117  8.661 0.07403
print("R IPS")
## [1] "R IPS"
R_IPS.aov <- aov(DFR_R_IPS ~ level, data=split_DFR_delay[["all"]])
summary(R_IPS.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.283  0.1414   1.846  0.162
## Residuals   117  8.962  0.0766
print("R medial Parietal")
## [1] "R medial Parietal"
R_medParietal.aov <- aov(DFR_R_medParietal ~ level, data=split_DFR_delay[["all"]])
summary(R_medParietal.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.254  0.1271   0.611  0.545
## Residuals   117 24.350  0.2081

Probe Period

Full Mask

No differences in the probe period.

fullMask_delay_plots[["probe_low"]]+fullMask_delay_plots[["probe_high"]]+fullMask_delay_plots[["probe_loadEffect"]]+
  plot_annotation(title="BOLD signal from full delay period mask during probe period")

print("Low Load")
## [1] "Low Load"
probe_L1.aov <- aov(probe_low ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L1.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.18  0.0876    0.21  0.811
## Residuals   117  48.72  0.4164
print("High Load")
## [1] "High Load"
probe_L3.aov <- aov(probe_high ~ level, data=split_fullMask_delay[["all"]])
summary(probe_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   2.86  1.4324   1.948  0.147
## Residuals   117  86.02  0.7352
print("Load Effect")
## [1] "Load Effect"
probe_LE.aov <- aov(probe_loadEffect ~ level, data=split_fullMask_delay[["all"]])
summary(probe_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   2.22  1.1086    1.72  0.184
## Residuals   117  75.43  0.6447

Individual ROIs

DFR_probe_plots[["dmPFC_loadEffect"]] + DFR_probe_plots[["L_aMFG_loadEffect"]] + DFR_probe_plots[["L_dlPFC_loadEffect"]] +
  plot_annotation(title="individual DFR activity from probe period regions")

DFR_probe_plots[["L_insula_loadEffect"]] + DFR_probe_plots[["L_IPS_loadEffect"]] + DFR_probe_plots[["R_dlPFC_loadEffect"]] 

DFR_probe_plots[["R_insula_loadEffect"]] + DFR_probe_plots[["R_OFC_loadEffect"]] 

print("dmPFC")
## [1] "dmPFC"
probe_dmPFC.aov <- aov(dmPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_dmPFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.71  0.3534   1.154  0.319
## Residuals   117  35.84  0.3063
print("L aMFG")
## [1] "L aMFG"
probe_L_aMFG.aov <- aov(L_aMFG_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_aMFG.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.84  0.4216   0.558  0.574
## Residuals   117  88.40  0.7556
print("L dlPFC")
## [1] "L dlPFC"
probe_L_dlPFC.aov <- aov(L_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_dlPFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.75  0.3734   0.819  0.443
## Residuals   117  53.34  0.4559
print("L insula")
## [1] "L insula"
probe_L_insula.aov <- aov(L_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_L_insula.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.067 0.03365   0.177  0.838
## Residuals   117 22.286 0.19048
print("R dlPFC")
## [1] "R dlPFC"
probe_R_dlPFC.aov <- aov(R_dlPFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_dlPFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.22  0.1101   0.269  0.765
## Residuals   117  47.94  0.4097
print("R Insula")
## [1] "R Insula"
probe_R_insula.aov <- aov(R_insula_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_insula.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.087 0.04338   0.246  0.782
## Residuals   117 20.620 0.17624
print("R OFC")
## [1] "R OFC"
probe_R_OFC.aov <- aov(R_OFC_loadEffect ~ level, data=split_DFR_probe[["all"]])
summary(probe_R_OFC.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.235  0.1174   0.562  0.571
## Residuals   117 24.427  0.2088

FFA

No differences.

DFR_FFA_plots[["L_CUE_LE"]] + DFR_FFA_plots[["L_DELAY_LE"]] + DFR_FFA_plots[["L_PROBE_LE"]]+
  plot_annotation(title="FFA during DFR task")

DFR_FFA_plots[["R_CUE_LE"]] + DFR_FFA_plots[["R_DELAY_LE"]] + DFR_FFA_plots[["R_PROBE_LE"]]

print("L Cue")
## [1] "L Cue"
L_CUE_LE_FFA.aov <- aov(L_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_CUE_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.93  0.4650    1.57  0.212
## Residuals   116  34.35  0.2961
print("R Cue")
## [1] "R Cue"
R_CUE_LE_FFA.aov <- aov(R_CUE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_CUE_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.24  0.1222   0.427  0.654
## Residuals   116  33.21  0.2863
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_FFA.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_DELAY_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.007 0.00374   0.086  0.918
## Residuals   116  5.051 0.04354
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_FFA.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_DELAY_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.002 0.00091   0.023  0.977
## Residuals   116  4.533 0.03908
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_FFA.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(L_PROBE_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   2.18  1.0919   1.666  0.194
## Residuals   116  76.04  0.6555
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_FFA.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_FFA[["all"]])
summary(R_PROBE_LE_FFA.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.32  0.6596   1.256  0.289
## Residuals   116  60.90  0.5250

HPC

Seeing differences in HPC activity in posterior segment for cue and probe L3.

Anterior

L3

DFR_HPC_Ant_plots[["L_CUE_L3"]] + DFR_HPC_Ant_plots[["L_DELAY_L3"]] + DFR_HPC_Ant_plots[["L_PROBE_L3"]]+
  plot_annotation(title="HPC Ant during DFR task")

DFR_HPC_Ant_plots[["R_CUE_L3"]] + DFR_HPC_Ant_plots[["R_DELAY_L3"]] + DFR_HPC_Ant_plots[["R_PROBE_L3"]]

print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Ant.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.179 0.08956   0.387   0.68
## Residuals   116 26.867 0.23162
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Ant.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.056 0.02785   0.133  0.875
## Residuals   116 24.262 0.20915
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Ant.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_L3_HPC_Ant.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2  0.020 0.009807   0.328  0.721
## Residuals   116  3.469 0.029902
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Ant.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0844 0.04219   1.657  0.195
## Residuals   116 2.9526 0.02545
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Ant.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.56  0.2800   0.545  0.582
## Residuals   116  59.64  0.5141
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Ant.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_L3_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.08  0.0410   0.071  0.931
## Residuals   116  66.77  0.5756

LE

DFR_HPC_Ant_plots[["L_CUE_LE"]] + DFR_HPC_Ant_plots[["L_DELAY_LE"]] + DFR_HPC_Ant_plots[["L_PROBE_LE"]]+
  plot_annotation(title="HPC Ant during DFR task")

DFR_HPC_Ant_plots[["R_CUE_LE"]] + DFR_HPC_Ant_plots[["R_DELAY_LE"]] + DFR_HPC_Ant_plots[["R_PROBE_LE"]]

print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Ant.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_CUE_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.421  0.2103   1.249  0.291
## Residuals   116 19.534  0.1684
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Ant.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_CUE_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.165 0.08263   0.451  0.638
## Residuals   116 21.248 0.18317
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Ant.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_DELAY_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.025 0.01240   0.369  0.692
## Residuals   116  3.896 0.03359
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Ant.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_DELAY_LE_HPC_Ant.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2  0.004 0.001886   0.061  0.941
## Residuals   116  3.588 0.030928
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Ant.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(L_PROBE_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.20  0.1014   0.199   0.82
## Residuals   116  59.16  0.5100
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Ant.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Ant[["all"]])
summary(R_PROBE_LE_HPC_Ant.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.51  0.2553   0.521  0.595
## Residuals   116  56.84  0.4900

Medial

L3

DFR_HPC_Med_plots[["L_CUE_L3"]] + DFR_HPC_Med_plots[["L_DELAY_L3"]] + DFR_HPC_Med_plots[["L_PROBE_L3"]]+
  plot_annotation(title="HPC_Med during DFR task")

DFR_HPC_Med_plots[["R_CUE_L3"]] + DFR_HPC_Med_plots[["R_DELAY_L3"]] + DFR_HPC_Med_plots[["R_PROBE_L3"]]

print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Med.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.099 0.04973   0.316   0.73
## Residuals   116 18.266 0.15746
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Med.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.289  0.1444   0.908  0.406
## Residuals   116 18.450  0.1590
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Med.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_L3_HPC_Med.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0171 0.008561   0.381  0.684
## Residuals   116 2.6062 0.022467
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Med.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0707 0.03535    1.81  0.168
## Residuals   116 2.2654 0.01953
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Med.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.89  0.4462   1.025  0.362
## Residuals   116  50.48  0.4351
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Med.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_L3_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.11  0.5540   1.232  0.295
## Residuals   116  52.16  0.4497

LE

DFR_HPC_Med_plots[["L_CUE_LE"]] + DFR_HPC_Med_plots[["L_DELAY_LE"]] + DFR_HPC_Med_plots[["L_PROBE_LE"]]+
  plot_annotation(title="HPC_Med during DFR task")

DFR_HPC_Med_plots[["R_CUE_LE"]] + DFR_HPC_Med_plots[["R_DELAY_LE"]] + DFR_HPC_Med_plots[["R_PROBE_LE"]]

print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Med.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_CUE_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.01 0.00489   0.049  0.952
## Residuals   116  11.51 0.09919
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Med.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_CUE_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.053 0.02625   0.236   0.79
## Residuals   116 12.919 0.11137
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Med.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_DELAY_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0314 0.01569   0.589  0.557
## Residuals   116 3.0904 0.02664
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Med.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_DELAY_LE_HPC_Med.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0022 0.001118   0.049  0.952
## Residuals   116 2.6589 0.022921
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Med.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(L_PROBE_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.67  0.3349   0.816  0.445
## Residuals   116  47.61  0.4104
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Med.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Med[["all"]])
summary(R_PROBE_LE_HPC_Med.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.13  0.5658   1.713  0.185
## Residuals   116  38.31  0.3302

Posterior

L3

DFR_HPC_Post_plots[["L_CUE_L3"]] + DFR_HPC_Post_plots[["L_DELAY_L3"]] + DFR_HPC_Post_plots[["L_PROBE_L3"]]+
  plot_annotation(title="HPC_Post during DFR task")

DFR_HPC_Post_plots[["R_CUE_L3"]] + DFR_HPC_Post_plots[["R_DELAY_L3"]] + DFR_HPC_Post_plots[["R_PROBE_L3"]]

print("L Cue")
## [1] "L Cue"
L_CUE_L3_HPC_Post.aov <- aov(L_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  1.115  0.5577    4.26 0.0164 *
## Residuals   116 15.186  0.1309                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_CUE_L3_HPC_Post.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = L_CUE_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
## 
## $level
##                diff         lwr          upr     p adj
## med-high  0.2136174  0.02153523  0.405699632 0.0253429
## low-high  0.0188655 -0.17444407  0.212175078 0.9708461
## low-med  -0.1947519 -0.38806150 -0.001442354 0.0478814
print("R Cue")
## [1] "R Cue"
R_CUE_L3_HPC_Post.aov <- aov(R_CUE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.667  0.3334   2.727 0.0696 .
## Residuals   116 14.181  0.1222                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("L Delay")
## [1] "L Delay"
L_DELAY_L3_HPC_Post.aov <- aov(L_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0579 0.02895   2.053  0.133
## Residuals   116 1.6353 0.01410
print("R Delay")
## [1] "R Delay"
R_DELAY_L3_HPC_Post.aov <- aov(R_DELAY_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0276 0.01379   1.182   0.31
## Residuals   116 1.3534 0.01167
print("L Probe")
## [1] "L Probe"
L_PROBE_L3_HPC_Post.aov <- aov(L_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value  Pr(>F)   
## level         2   2.87  1.4366   5.043 0.00794 **
## Residuals   116  33.04  0.2849                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(L_PROBE_L3_HPC_Post.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = L_PROBE_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
## 
## $level
##                diff         lwr         upr     p adj
## med-high  0.2358601 -0.04748753  0.51920767 0.1226631
## low-high -0.1412081 -0.42636627  0.14395003 0.4700479
## low-med  -0.3770682 -0.66222634 -0.09191004 0.0060407
print("R Probe")
## [1] "R Probe"
R_PROBE_L3_HPC_Post.aov <- aov(R_PROBE_L3 ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_L3_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2   2.06  1.0315   3.368 0.0379 *
## Residuals   116  35.53  0.3063                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(R_PROBE_L3_HPC_Post.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = R_PROBE_L3 ~ level, data = split_DFR_HPC_Post[["all"]])
## 
## $level
##                diff        lwr         upr     p adj
## med-high  0.1935997 -0.1002181  0.48741756 0.2652144
## low-high -0.1270240 -0.4227192  0.16867132 0.5658518
## low-med  -0.3206237 -0.6163190 -0.02492841 0.0301680

LE

DFR_HPC_Post_plots[["L_CUE_LE"]] + DFR_HPC_Post_plots[["L_DELAY_LE"]] + DFR_HPC_Post_plots[["L_PROBE_LE"]]+
  plot_annotation(title="HPC_Post during DFR task")

DFR_HPC_Post_plots[["R_CUE_LE"]] + DFR_HPC_Post_plots[["R_DELAY_LE"]] + DFR_HPC_Post_plots[["R_PROBE_LE"]]

print("L Cue")
## [1] "L Cue"
L_CUE_LE_HPC_Post.aov <- aov(L_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_CUE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.223 0.11161   1.386  0.254
## Residuals   116  9.341 0.08053
print("R Cue")
## [1] "R Cue"
R_CUE_LE_HPC_Post.aov <- aov(R_CUE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_CUE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.290  0.1449   2.147  0.121
## Residuals   116  7.829  0.0675
print("L Delay")
## [1] "L Delay"
L_DELAY_LE_HPC_Post.aov <- aov(L_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_DELAY_LE_HPC_Post.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0021 0.001067   0.051   0.95
## Residuals   116 2.4087 0.020764
print("R Delay")
## [1] "R Delay"
R_DELAY_LE_HPC_Post.aov <- aov(R_DELAY_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_DELAY_LE_HPC_Post.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2  0.002 0.000985   0.068  0.935
## Residuals   116  1.687 0.014546
print("L Probe")
## [1] "L Probe"
L_PROBE_LE_HPC_Post.aov <- aov(L_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(L_PROBE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   1.13  0.5645   2.037  0.135
## Residuals   116  32.15  0.2771
print("R Probe")
## [1] "R Probe"
R_PROBE_LE_HPC_Post.aov <- aov(R_PROBE_LE ~ level, data=split_DFR_HPC_Post[["all"]])
summary(R_PROBE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  1.437  0.7184   2.796 0.0652 .
## Residuals   116 29.804  0.2569                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Cortical Thickness

Only see differences in the L probe regions, with med > high.

cortical_thickness_plots[["Cue_RH"]] + cortical_thickness_plots[["Delay_RH"]] + cortical_thickness_plots[["Probe_RH"]] + 
  plot_annotation(title="Cortical Thickness from DFR Full Mask") 

cortical_thickness_plots[["Cue_LH"]] + cortical_thickness_plots[["Delay_LH"]] + cortical_thickness_plots[["Probe_LH"]]

print("L Cue")
## [1] "L Cue"
L_CUE_DFR_thick.aov <- aov(Cue_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_CUE_DFR_thick.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0037 0.001853   0.199   0.82
## Residuals   116 1.0818 0.009326
print("R Cue")
## [1] "R Cue"
R_CUE_DFR_thick.aov <- aov(Cue_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_CUE_LE_HPC_Post.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.290  0.1449   2.147  0.121
## Residuals   116  7.829  0.0675
print("L Delay")
## [1] "L Delay"
L_DELAY_DFR_thick.aov <- aov(Delay_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_DELAY_DFR_thick.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0366 0.01829   0.804   0.45
## Residuals   116 2.6380 0.02274
print("R Delay")
## [1] "R Delay"
R_DELAY_DFR_thick.aov <- aov(Delay_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_DELAY_DFR_thick.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0012 0.000591   0.034  0.966
## Residuals   116 2.0093 0.017322
print("L Probe")
## [1] "L Probe"
L_PROBE_DFR_thick.aov <- aov(Probe_LH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(L_PROBE_DFR_thick.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2 0.0294 0.01472   0.862  0.425
## Residuals   116 1.9809 0.01708
print("R Probe")
## [1] "R Probe"
R_PROBE_DFR_thick.aov <- aov(Probe_RH ~ level, data=split_cortical_thickness_DFR[["all"]])
summary(R_PROBE_DFR_thick.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0124 0.006181   0.333  0.717
## Residuals   116 2.1502 0.018536

Resting State Functional Connectivity

Within Network

No differences.

RS_plots[["FPCN_FPCN"]] + RS_plots[["DMN_DMN"]] + RS_plots[["DAN_DAN"]]+
  plot_annotation(title="Resting State Functional Connectivity - Within Networks")

RS_plots[["VAN_VAN"]] + RS_plots[["CO_CO"]] + RS_plots[["visual_visual"]]

print("FPCN")
## [1] "FPCN"
FPCN.aov <- aov(FPCN_FPCN ~ level, data=split_RS[["all"]])
summary(FPCN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0010 0.000493   0.062  0.939
## Residuals   116 0.9151 0.007888
print("DMN")
## [1] "DMN"
DMN.aov <- aov(DMN_DMN ~ level, data=split_RS[["all"]])
summary(DMN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0112 0.005622   0.562  0.572
## Residuals   116 1.1607 0.010006
print("DAN")
## [1] "DAN"
DAN.aov <- aov(DAN_DAN ~ level, data=split_RS[["all"]])
summary(DAN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0031 0.001532   0.249   0.78
## Residuals   116 0.7133 0.006149
print("VAN")
## [1] "VAN"
VAN.aov <- aov(VAN_VAN ~ level, data=split_RS[["all"]])
summary(VAN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)  
## level         2  0.043 0.021483   2.475 0.0886 .
## Residuals   116  1.007 0.008679                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("CO")
## [1] "CO"
CO.aov <- aov(CO_CO ~ level, data=split_RS[["all"]])
summary(CO.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0006 0.000289    0.04  0.961
## Residuals   116 0.8374 0.007219
print("CO")
## [1] "CO"
visual.aov <- aov(visual_visual ~ level, data=split_RS[["all"]])
summary(visual.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2  0.019 0.009497   0.528  0.591
## Residuals   116  2.086 0.017981

Across Network

No across RS network differences.

RS_plots[["FPCN_DMN"]] + RS_plots[["FPCN_DAN"]] + RS_plots[["FCPN_VAN"]]+
  plot_annotation(title="Resting State Functional Connectivity - Across Networks")

RS_plots[["FPCN_CO"]] + RS_plots[["FPCN_visual"]]

print("FPCN DMN")
## [1] "FPCN DMN"
FPCN_DMN.aov <- aov(FPCN_DMN ~ level, data=split_RS[["all"]])
summary(FPCN_DMN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0155 0.007775   1.002   0.37
## Residuals   116 0.8997 0.007756
print("FPCN DAN")
## [1] "FPCN DAN"
FPCN_DAN.aov <- aov(FPCN_DAN ~ level, data=split_RS[["all"]])
summary(FPCN_DAN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0034 0.001689   0.374  0.689
## Residuals   116 0.5237 0.004515
print("FPCN VAN")
## [1] "FPCN VAN"
FPCN_VAN.aov <- aov(FPCN_VAN ~ level, data=split_RS[["all"]])
summary(FPCN_VAN.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0189 0.009461   1.505  0.226
## Residuals   116 0.7290 0.006284
print("FPCN CO")
## [1] "FPCN CO"
FPCN_CO.aov <- aov(FPCN_CO ~ level, data=split_RS[["all"]])
summary(FPCN_CO.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)  
## level         2 0.0507 0.025374    2.62 0.0771 .
## Residuals   116 1.1234 0.009685                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("FPCN visual")
## [1] "FPCN visual"
FPCN_visual.aov <- aov(FPCN_visual ~ level, data=split_RS[["all"]])
summary(FPCN_visual.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)  
## level         2 0.0374 0.018712   3.013  0.053 .
## Residuals   116 0.7205 0.006211                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Beta Series Connectivity

Cue Period

High Load

No differences.

beta_conn_cue_plots[["FPCN_FPCN_L3"]] + beta_conn_cue_plots[["FPCN_HPC_L3"]] +
  plot_annotation(title = "Beta Series Connectivity at High Load")

beta_conn_cue_plots[["FPCN_FFA_L3"]] + beta_conn_cue_plots[["HPC_FFA_L3"]]

FPCN_FPCN_BC_cue_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.193 0.09666   0.779  0.461
## Residuals   116 14.397 0.12411
FPCN_HPC_BC_cue_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.043 0.02156   0.206  0.814
## Residuals   116 12.146 0.10471
FPCN_FFA_BC_cue_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.030 0.01476   0.187   0.83
## Residuals   116  9.161 0.07897
HPC_FFA_BC_cue_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.056 0.02784    0.28  0.757
## Residuals   116 11.551 0.09958

Load Effect

No differences.

beta_conn_cue_plots[["FPCN_FPCN_LE"]] + beta_conn_cue_plots[["FPCN_HPC_LE"]] +
  plot_annotation(title = "Beta Series Connectivity Load Effect")

beta_conn_cue_plots[["FPCN_FFA_LE"]] + beta_conn_cue_plots[["HPC_FFA_LE"]]

FPCN_FPCN_BC_cue_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FPCN_BC_cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.568  0.2839   1.678  0.191
## Residuals   116 19.628  0.1692
FPCN_HPC_BC_cue_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_HPC_BC_cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.085 0.04247   0.343   0.71
## Residuals   116 14.345 0.12367
FPCN_FFA_BC_cue_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(FPCN_FFA_BC_cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.08 0.03981   0.234  0.792
## Residuals   116  19.71 0.16996
HPC_FFA_BC_cue_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_cue[["all"]])
summary(HPC_FFA_BC_cue_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.225  0.1123   0.681  0.508
## Residuals   116 19.133  0.1649

Delay Period

No differences for the beta series connectivity during delay period.

High Load

beta_conn_delay_plots[["FPCN_FPCN_L3"]] + beta_conn_delay_plots[["FPCN_HPC_L3"]] +
  plot_annotation(title = "Beta Series Connectivity at High Load")

beta_conn_delay_plots[["FPCN_FFA_L3"]] + beta_conn_delay_plots[["HPC_FFA_L3"]]

FPCN_FPCN_BC_delay_L3.aov <- aov(FPCN_FPCN_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.25 0.12487   1.402   0.25
## Residuals   116  10.33 0.08907
FPCN_HPC_BC_delay_L3.aov <- aov(FPCN_HPC_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.219  0.1093   1.757  0.177
## Residuals   116  7.215  0.0622
FPCN_FFA_BC_delay_L3.aov <- aov(FPCN_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.043 0.02170   0.364  0.696
## Residuals   116  6.920 0.05965
HPC_FFA_BC_delay_L3.aov <- aov(HPC_FFA_L3 ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_L3.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.170 0.08476   1.387  0.254
## Residuals   116  7.088 0.06111

Load Effect

Differences between the HPC/FFA connectivity - low > med.

beta_conn_delay_plots[["FPCN_FPCN_LE"]] + beta_conn_delay_plots[["FPCN_HPC_LE"]] +
  plot_annotation(title = "Beta Series Connectivity Load Effect")

beta_conn_delay_plots[["FPCN_FFA_LE"]] + beta_conn_delay_plots[["HPC_FFA_LE"]]

FPCN_FPCN_BC_delay_LE.aov <- aov(FPCN_FPCN_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FPCN_BC_delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.050 0.02517   0.536  0.587
## Residuals   116  5.452 0.04700
FPCN_HPC_BC_delay_LE.aov <- aov(FPCN_HPC_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_HPC_BC_delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.095 0.04745   0.745  0.477
## Residuals   116  7.389 0.06370
FPCN_FFA_BC_delay_LE.aov <- aov(FPCN_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(FPCN_FFA_BC_delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.114 0.05696   0.769  0.466
## Residuals   116  8.594 0.07408
HPC_FFA_BC_delay_LE.aov <- aov(HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
summary(HPC_FFA_BC_delay_LE.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  0.821  0.4105   4.664 0.0113 *
## Residuals   116 10.210  0.0880                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(HPC_FFA_BC_delay_LE.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = HPC_FFA_LE ~ level, data = split_beta_conn_delay[["all"]])
## 
## $level
##                 diff         lwr         upr     p adj
## med-high  0.07253329 -0.08496399  0.23003057 0.5200801
## low-high -0.12896551 -0.28746918  0.02953816 0.1343892
## low-med  -0.20149880 -0.36000247 -0.04299513 0.0087169

BCT Measures

No differences in any of the BCT measures.

Overall Measures

BCT_plots[["Participation_Coef_Mean"]] + BCT_plots[["Global_Eff"]] + BCT_plots[["Modularity_Louvain_N"]]+
  plot_annotation(title="Overall BCT Measures")

print("Mean Participation Coefficient")
## [1] "Mean Participation Coefficient"
partic_coef_mean.aov <- aov(Participation_Coef_Mean ~ level, data = split_BCT[["all"]])
summary(partic_coef_mean.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.125 0.06255   0.859  0.426
## Residuals   116  8.446 0.07281
print("Global Efficiency")
## [1] "Global Efficiency"
global_eff.aov <- aov(Global_Eff ~ level, data = split_BCT[["all"]])
summary(global_eff.aov)
##              Df Sum Sq  Mean Sq F value Pr(>F)
## level         2 0.0138 0.006884   1.857  0.161
## Residuals   116 0.4301 0.003708
print("Modularity")
## [1] "Modularity"
modularity.aov <- aov(Modularity_Louvain_N ~ level, data = split_BCT[["all"]])
summary(modularity.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2    0.3   0.128   0.017  0.983
## Residuals   116  869.9   7.499

Individual Networks

indiv_partic_coeff_plots[["FrontoParietal"]] + indiv_partic_coeff_plots[["Default"]] + indiv_partic_coeff_plots[["DorsalAttn"]]+
  plot_annotation(title="Individual Network Participation Coefficient")

indiv_partic_coeff_plots[["CinguloOperc"]] + indiv_partic_coeff_plots[["VentralAttn"]] + indiv_partic_coeff_plots[["Visual"]]

print("FPCN")
## [1] "FPCN"
FPCN_indiv_coeff.aov <- aov(FrontoParietal ~ level, data = split_indiv_partic_coeff[["all"]])
summary(FPCN_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.514  0.2568   1.391  0.253
## Residuals   116 21.411  0.1846
print("DMN")
## [1] "DMN"
DMN_indiv_coeff.aov <- aov(Default ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DMN_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)  
## level         2  1.119  0.5597   2.784 0.0659 .
## Residuals   116 23.319  0.2010                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print("DAN")
## [1] "DAN"
DAN_indiv_coeff.aov <- aov(DorsalAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(DAN_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.043 0.02172   0.237  0.789
## Residuals   116 10.623 0.09158
print("CO")
## [1] "CO"
CO_indiv_coeff.aov <- aov(CinguloOperc ~ level, data = split_indiv_partic_coeff[["all"]])
summary(CO_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2  0.318  0.1588   1.354  0.262
## Residuals   116 13.611  0.1173
print("VAN")
## [1] "VAN"
VAN_indiv_coeff.aov <- aov(VentralAttn ~ level, data = split_indiv_partic_coeff[["all"]])
summary(VAN_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.50  0.2499   2.108  0.126
## Residuals   116  13.75  0.1185
print("visual")
## [1] "visual"
visual_indiv_coeff.aov <- aov(Visual ~ level, data = split_indiv_partic_coeff[["all"]])
summary(visual_indiv_coeff.aov)
##              Df Sum Sq Mean Sq F value Pr(>F)
## level         2   0.62  0.3102   1.918  0.151
## Residuals   116  18.75  0.1617